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- capacity control (1)
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The dissertation aims at investigating how information about jobs arriving to a service facility in the future can be used for capacity planning and control. Nowadays, technical equipment such as aircraft engines are equipped with sensors transferring condition data to central data warehouses in real-time. By jointly analyzing condition data and future usage information with machine learning algorithms, future equipment conditions and maintenance requirements can be forecasted. In the thesis, information regarding the arrival times of aircraft engine at a maintenance facility and the corresponding service requirements are used in order to optimally plan and control the flexible capacity of the facility. Queueing models are developed and analyzed to optimally size and control the facility's capacity and determine the implications on cost and job waiting time. It is demonstrated analytically and numerically that cost and waiting time can be reduced significantly when future information is available.